Performance of neural classifiers for fabric faults classification

Abstract

In this paper, fabric faults classification using CNeT (Behnke and Karayiannis, 1998) is studied. The basic objectives are to improve the features selection used in CNeT (Behnke and Karayiannis, 1998) classifier and compare the results with other neural network classifiers. The algorithm adopted here is composed of three stages. The first stage is a preprocessing phase where defects are detected and localized. Since every detected defect has its different shape and size, all defects are normalized to a predetermined size. In the second stage a set of features are calculated for each defect using the Haralick (1973, 1979) spatial features. The improved classification performance is achieved by employing a statistical method to select the most important features that can be used in classification. This is done by calculating a classification factor (Milligan and Cooper, 1985) for each feature vector to determine its effect in the classification process. During the third and last stage, those features are then used to train a competitive neural tree (CNeT) (Behnke and Karayiannis, 1998) designed to learn in a supervised manner the class associated with each set of features. The network can be then used to test and classify new defects. The approach is experimented with a set of images of fault free and faulty textiles and output results are compared with radial basis function classifiers.